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Volatility in Thailand Stock Market Using High-Frequency Data

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Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

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Abstract

The objective of this research is twofold: First, we aim to investigate the performance of conventional GARCH and GARCH-jump models when the data has high frequency. Second, the obtained conditional volatility from the best fit model is used to forecast and matched with the macroeconomic news announcement. We use GARCH and GARCH-jump models with high-frequency dataset of log return of Thailand stock market index (SET) from January, 2008 to December, 2015. We find that the volatility estimations by these two models have the same pattern but volatility estimation by GARCH-jump is higher than conventional GARCH model. However, the GARCH (1,1) and GARCH (1,1)-jump performances are non-stationary to estimate the volatility for 5 min interval return of SET but are stationary to estimate for 15 min, 30 min, 1 h, and 2 h returns of SET. Our results also show the matching jump point with macroeconomic news announcement. The empirical results support our assumption that macroeconomic news announcement may lead to volatility change in SET.

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Notes

  1. 1.

    World Bank [1] “Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.”

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Acknowledgement

We gratefully acknowledge the support and generosity of Chiang Mai University and Centre of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, without which the present study could not have been completed.

Author information

Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, 52000, Thailand

    Saowaluk Duangin

  2. Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai, 52000, Thailand

    Jirakom Sirisrisakulchai & Songsak Sriboonchitta

Authors
  1. Saowaluk Duangin
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  2. Jirakom Sirisrisakulchai
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  3. Songsak Sriboonchitta
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Corresponding author

Correspondence to Saowaluk Duangin .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

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Duangin, S., Sirisrisakulchai, J., Sriboonchitta, S. (2018). Volatility in Thailand Stock Market Using High-Frequency Data. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-70942-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

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